A Novel Model Based on DA-RNN Network and Skip Gated Recurrent Neural Network for Periodic Time Series Forecasting

Bingqing Huang, Haonan Zheng, Xinbo Guo, Yi Yang and Ximing Liu
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Bingqing Huang: School of Science, Rensselaer Polytechnic Institute, New York, NY 12180, USA
Haonan Zheng: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
Xinbo Guo: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
Yi Yang: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
Ximing Liu: School of Management, Hefei University of Technology, Hefei 230002, China

Sustainability, 2021, vol. 14, issue 1, 1-14

Abstract: Deep learning models are playing an increasingly important role in time series forecasting with their excellent predictive ability and the convenience of not requiring complex feature engineering. However, the existing deep learning models still have shortcomings in dealing with periodic and long-distance dependent sequences, which lead to unsatisfactory forecasting performance on this type of dataset. To handle these two issues better, this paper proposes a novel periodic time series forecasting model based on DA-RNN, called DA-SKIP. Using the idea of task decomposition, the novel model, based on DA-RNN, GRU-SKIP and autoregressive component, breaks down the prediction of periodic time series into three parts: linear forecasting, nonlinear forecasting and periodic forecasting. The results of the experiments on Solar Energy, Electricity Consumption and Air Quality datasets show that the proposed model outperforms the three comparison models in capturing periodicity and long-distance dependence features of sequences.

Keywords: time series; prediction; deep learning; attention; recurrent neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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